Precision tuning of a page or word of non-volatile memory cells in an analog neural memory system

ABSTRACT

Numerous examples for performing tuning of a page or a word of non-volatile memory cells in an analog neural memory are disclosed. In one example, a method comprises programming a word or page of non-volatile memory cells in an analog neural memory system; and identifying any fast bits in the word or page of non-volatile memory cells.

PRIORITY CLAIM

This application is a divisional of U.S. patent application Ser. No.17/024,410, filed on Sep. 17, 2020, and titled, “Precision Tuning of aPage or Word of Non-Volatile Memory Cells And Associated High VoltageCircuits for an Analog Neural Memory Array in an Artificial NeuralNetwork,” which claims priority to U.S. Provisional Patent ApplicationNo. 62/993,008, filed on Mar. 22, 2020, and titled “Precision Tuning ofa Page or Word of Non-Volatile Memory Cells And Associated High VoltageCircuits for an Analog Neural Memory Array in an Artificial NeuralNetwork,” which are incorporated by reference herein.

FIELD OF THE INVENTION

Numerous examples for performing precision tuning of a page or word ofnon-volatile memory cells in an analog neural memory system aredisclosed.

BACKGROUND OF THE INVENTION

Artificial neural networks mimic biological neural networks (the centralnervous systems of animals, in particular the brain) and are used toestimate or approximate functions that can depend on a large number ofinputs and are generally unknown. Artificial neural networks generallyinclude layers of interconnected “neurons” which exchange messagesbetween each other.

FIG. 1 illustrates an artificial neural network, where the circlesrepresent the inputs or layers of neurons. The connections (calledsynapses) are represented by arrows, and have numeric weights that canbe tuned based on experience. This makes the artificial neural networkadaptive to inputs and capable of learning. Typically, artificial neuralnetworks include a layer of multiple inputs. There are typically one ormore intermediate layers of neurons, and an output layer of neurons thatprovide the output of the neural network. The neurons at each levelindividually or collectively make a decision based on the received datafrom the synapses.

One of the major challenges in the development of artificial neuralnetworks for high-performance information processing is a lack ofadequate hardware technology. Indeed, practical artificial neuralnetworks rely on a very large number of synapses, enabling highconnectivity between neurons, i.e. a very high computationalparallelism. In principle, such complexity can be achieved with digitalsupercomputers or specialized graphics processing unit clusters.However, in addition to high cost, these approaches also suffer frommediocre energy efficiency as compared to biological networks, whichconsume much less energy primarily because they perform low-precisionanalog computation. CMOS analog circuits have been used for artificialneural networks, but most CMOS-implemented synapses have been too bulkygiven the high number of neurons and synapses.

Applicant previously disclosed an artificial (analog) neural networkthat utilizes one or more non-volatile memory arrays as the synapses inU.S. patent application Ser. No. 15/594,439, published as US PatentPublication 2017/0337466, which is incorporated by reference. Thenon-volatile memory arrays operate as an analog neuromorphic memory. Theterm neuromorphic, as used herein, means circuitry that implement modelsof neural systems. The analog neuromorphic memory includes a firstplurality of synapses configured to receive a first plurality of inputsand to generate therefrom a first plurality of outputs, and a firstplurality of neurons configured to receive the first plurality ofoutputs. The first plurality of synapses includes a plurality of memorycells, wherein each of the memory cells includes spaced apart source anddrain regions formed in a semiconductor substrate with a channel regionextending there between, a floating gate disposed over and insulatedfrom a first portion of the channel region and a non-floating gatedisposed over and insulated from a second portion of the channel region.Each of the plurality of memory cells is configured to store a weightvalue corresponding to a number of electrons on the floating gate. Theplurality of memory cells is configured to multiply the first pluralityof inputs by the stored weight values to generate the first plurality ofoutputs. An array of memory cells arranged in this manner can bereferred to as a vector by matrix multiplication (VMM) array.

Examples of different non-volatile memory cells that can be used in VMMswill now be discussed.

Non-Volatile Memory Cells

Various types of known non-volatile memory cells can be used in the VMMarrays. For example, U.S. Pat. No. 5,029,130 (“the '130 patent”), whichis incorporated herein by reference, discloses an array of split gatenon-volatile memory cells, which are a type of flash memory cells. Sucha memory cell 210 is shown in FIG. 2. Each memory cell 210 includessource region 14 and drain region 16 formed in semiconductor substrate12, with channel region 18 there between. Floating gate 20 is formedover and insulated from (and controls the conductivity of) a firstportion of the channel region 18, and over a portion of the sourceregion 14. Word line terminal 22 (which is typically coupled to a wordline) has a first portion that is disposed over and insulated from (andcontrols the conductivity of) a second portion of the channel region 18,and a second portion that extends up and over the floating gate 20. Thefloating gate 20 and word line terminal 22 are insulated from thesubstrate 12 by a gate oxide. Bitline terminal 24 is coupled to drainregion 16.

Memory cell 210 is erased (where electrons are removed from the floatinggate) by placing a high positive voltage on the word line terminal 22,which causes electrons on the floating gate 20 to tunnel through theintermediate insulation from the floating gate 20 to the word lineterminal 22 via Fowler-Nordheim tunneling.

Memory cell 210 is programmed (where electrons are placed on thefloating gate) by placing a positive voltage on the word line terminal22, and a positive voltage on the source region 14. Electron currentwill flow from the drain region 16 towards the source region 14 (sourceline terminal). The electrons will accelerate and become energized(heated) when they reach the gap between the word line terminal 22 andthe floating gate 20. Some of the heated electrons will be injectedthrough the gate oxide onto the floating gate 20 due to the attractiveelectrostatic force from the floating gate 20.

Memory cell 210 is read by placing positive read voltages on the drainregion 16 and word line terminal 22 (which turns on the portion of thechannel region 18 under the word line terminal). If the floating gate 20is positively charged (i.e. erased of electrons), then the portion ofthe channel region 18 under the floating gate 20 is turned on as well,and current will flow across the channel region 18, which is sensed asthe erased or “1” state. If the floating gate 20 is negatively charged(i.e. programmed with electrons), then the portion of the channel regionunder the floating gate 20 is mostly or entirely turned off, and currentwill not flow (or there will be little flow) across the channel region18, which is sensed as the programmed or “0” state.

Table No. 1 depicts typical voltage ranges that can be applied to theterminals of memory cell 110 for performing read, erase, and programoperations:

TABLE No. 1 Operation of Flash Memory Cell 210 of FIG. 2 WL BL SL Read 10.5-3 V 0.1-2 V 0 V Read 2 0.5-3 V 0-2 V 2-0.1 V     Erase ~11-13 V  0 V0 V Program  1-2 V 1-3 μA 9-10 V   “Read 1” is a read mode in which the cell current is output on the bitline. “Read 2” is a read mode in which the cell current is output on thesource line terminal.

FIG. 3 shows memory cell 310, which is similar to memory cell 210 ofFIG. 2 with the addition of control gate (CG) terminal 28. Control gateterminal 28 is biased at a high voltage, e.g., 10V, in programming, lowor negative in erase, e.g., 0 v/−8V, low or mid range in read, e.g., 0v/2.5V. Other terminals are biased similarly to that of FIG. 2.

FIG. 4 depicts four-gate memory cell 410 comprising source region 14,drain region 16, floating gate 20 over a first portion of channel region18, a select gate 22 (typically coupled to a word line, WL) over asecond portion of the channel region 18, a control gate 28 over thefloating gate 20, and an erase gate 30 over the source region 14. Thisconfiguration is described in U.S. Pat. No. 6,747,310, which isincorporated herein by reference for all purposes. Here, all gates arenon-floating gates except floating gate 20, meaning that they areelectrically connected or connectable to a voltage source. Programmingis performed by heated electrons from the channel region 18 injectingthemselves onto the floating gate 20. Erasing is performed by electronstunneling from the floating gate 20 to the erase gate 30.

Table No. 2 depicts typical voltage ranges that can be applied to theterminals of memory cell 410 for performing read, erase, and programoperations:

TABLE No. 2 Operation of Flash Memory Cell 410 of FIG. 4 WL/SG BL CG EGSL Read 1 0.5-2 V 0.1-2 V 0-2.6 V 0-2.6 V     0 V Read 2 0.5-2 V 0-2 V0-2.6 V 0-2.6 V 2-0.1 V Erase −0.5 V/0 V 0 V 0 V/−8 V  8-12 V     0 VProgram 1 V 1 μA 8-11 V 4.5-9 V 4.5-5 V“Read 1” is a read mode in which the cell current is output on the bitline. “Read 2” is a read mode in which the cell current is output on thesource line terminal.

FIG. 5 shows memory cell 510, which is similar to memory cell 410 ofFIG. 4 except that memory cell 510 does not contain an erase gate EGterminal. An erase is performed by biasing the substrate 18 to a highvoltage and biasing the control gate CG terminal 28 to a low or negativevoltage. Alternatively, an erase is performed by biasing word lineterminal 22 to a positive voltage and biasing control gate terminal 28to a negative voltage. Programming and reading is similar to that ofFIG. 4.

FIG. 6 depicts a three-gate memory cell 610, which is another type offlash memory cell. Memory cell 610 is identical to the memory cell 410of FIG. 4 except that memory cell 610 does not have a separate controlgate terminal. The erase operation (whereby erasing occurs through useof the erase gate terminal) and read operation are similar to that ofthe FIG. 4 except there is no control gate bias applied. The programmingoperation also is done without the control gate bias, and as a result, ahigher voltage must be applied on the source line terminal during aprogram operation to compensate for a lack of control gate bias.

Table No. 3 depicts typical voltage ranges that can be applied to theterminals of memory cell 610 for performing read, erase, and programoperations:

TABLE No. 3 Operation of Flash Memory Cell 610 of FIG. 6 WL/SG BL EG SLRead 1 0.5-2.2 V 0.1-2 V 0-2.6 V 0 V Read 2 0.5-2.2 V 0-2 V 0-2.6 V2-0.1 V     Erase −0.5 V/0 V 0 V  11.5 V 0 V Program 1 V 2-3 μA  4.5 V7-9 V “Read 1” is a read mode in which the cell current is output on the bitline. “Read 2” is a read mode in which the cell current is output on thesource line terminal.

FIG. 7 depicts stacked gate memory cell 710, which is another type offlash memory cell. Memory cell 710 is similar to memory cell 210 of FIG.2, except that floating gate 20 extends over the entire channel region18, and control gate terminal 22 (which here will be coupled to a wordline) extends over floating gate 20, separated by an insulating layer(not shown). Programming is performed using hot electron injection fromchannel 18 to floating gate 20 in the channel region next to the drainregion 16, and erasing is performed using by Fowler-Nordheim electrontunneling from floating gate 20 to substrate 12. The read operationsoperate in a similar manner to that described previously for memory cell210.

Table No. 4 depicts typical voltage ranges that can be applied to theterminals of memory cell 710 and substrate 12 for performing read,erase, and program operations:

TABLE No. 4 Operation of Flash Memory Cell 710 of FIG. 7 CG BL SLSubstrate Read 1 0-5 V 0.1-2 V 0-2 V 0 V Read 2 0.5-2 V 0-2 V 2-0.1 V 0V Erase −8 to −10 V/0 V FLT FLT 8-10 V/15-20 V Program 8-12 V 3-5 V/0 V0 V/3-5 V 0 V

“Read 1” is a read mode in which the cell current is output on the bitline. “Read 2” is a read mode in which the cell current is output on thesource line terminal. Optionally, in arrays comprising rows and columnsof memory cells 210, 310, 410, 510, 610, or 710, source lines can becoupled to one row of memory cells or to two adjacent rows of memorycells. That is, source line terminals can be shared by adjacent rows ofmemory cells.

FIG. 8 depicts twin split-gate memory cell 810. Memory cell 810comprises floating gate (FG) 20 disposed over and insulated from thesubstrate 12, a control gate 28 (CG) disposed over and insulated fromthe floating gate 20, an erase gate 30 (EG) disposed adjacent to andinsulated from the floating and control gates 20/28 and disposed overand insulated from the substrate 12, where the erase gate 30 is createdwith a T shape such that a top corner of the control gate CG faces theinside corner of the T shaped erase gate to improve erase efficiency,and a drain region 16 (DR) in the substrate adjacent the floating gate20 (with a bit line contact 24 (BL) connected to the drain diffusionregions 16 (DR)). The memory cells are formed as pairs of memory cells(A on the left and B on the right), sharing a common erase gate 30. Thiscell design differs from that the memory cells discussed above withreference to FIGS. 2-7 at least in that it lacks a source region underthe erase gate EG, lacks a select gate (also referred to as a wordline), and lacks a channel region for each memory cell. Instead, asingle continuous channel region 18 extends under both memory cells(i.e. extends from the drain region 16A of one memory cell to the drainregion 16B of the other memory cell). To read or program one memorycell, the control gate 28 of the other memory cell is raised to asufficient voltage to turn on the underlying channel region portion viavoltage coupling to the floating gate 20 there between (e.g. to read orprogram cell A, the voltage on FGB 20B is raised via voltage couplingfrom CGB 28B to turn on the channel region portion under FGB 20B).Erasing is performed using Fowler Nordheim electron tunneling fromfloating gate 20 to erase gate 30. Programming is performed using hotelectron injection from channel 18 to floating gate 20, this isindicated as PROGRAM 1 in Table 5. Alternatively programming isperformed using Fowler Nordheim electron tunneling from erase gate 30 tofloating gate 20, this is indicated as PROGRAM 2 in Table 5.Alternatively programming is performed using Fowler Nordheim electrontunneling from channel 18 to floating gate 20, in this case thecondition is similar to PROGRAM 2 except the substrate 12 is biased at alow voltage or negative voltage while erase gate 30 is biased at a lowpositive voltage.

Table No. 5 depicts typical voltage ranges that can be applied to theterminals of memory cell 810 for performing read, erase, and programoperations. In this Table, it is assumed that cell A (with terminals EG,CGA, and BLA) is selected for a read, program, or erase operation ineach row

TABLE No. 5 Operation of Flash Memory Cell 810 of FIG. 8 CGA BLA EG CGBBLB READ1 1.5-4 V 0.1-0.8 V    2.5 V 1.5-4 V 0 ERASE 0 V to −8 V   0 V 8V to 11.5 V 0 V to 4 V   0 V (Vcginhe) PROGRAM 1 10.5 V 4.5 V  1.5 V 4Iprog PROGRAM 2 4 V to 11.5 V 0 V −4 V to −11.5 V 0 V to −2 V 0 V(Vcginhp)

In order to utilize the memory arrays comprising one of the types ofnon-volatile memory cells described above in an artificial neuralnetwork, two modifications are made. First, the lines are configured sothat each memory cell can be individually programmed, erased, and readwithout adversely affecting the memory state of other memory cells inthe array, as further explained below. Second, continuous (analog)programming of the memory cells is provided.

Specifically, the memory state (i.e. charge on the floating gate) ofeach memory cell in the array can be continuously changed from a fullyerased state to a fully programmed state, independently and with minimaldisturbance of other memory cells. In another example, the memory state(i.e., charge on the floating gate) of each memory cell in the array canbe continuously changed from a fully programmed state to a fully erasedstate, and vice-versa, independently and with minimal disturbance ofother memory cells. This means the cell storage is analog or at the veryleast can store one of many discrete values (such as 16 or 64 differentvalues), which allows for very precise and individual tuning of all thecells in the memory array, and which makes the memory array ideal forstoring and making fine tuning adjustments to the synapsis weights ofthe neural network.

The methods and means described herein may apply to other non-volatilememory technologies such as FINFET split gate flash or stack gate flashmemory, NAND flash, 3D flash, SONOS(silicon-oxide-nitride-oxide-silicon, charge trap in nitride), MONOS(metal-oxide-nitride-oxide-silicon, metal charge trap in nitride), ReRAM(resistive ram), PCM (phase change memory), MRAM (magnetic ram), FeRAM(ferroelectric ram), OTP (bi-level or multi-level one timeprogrammable), and CeRAM (correlated electron ram), without limitation.The methods and means described herein may apply to volatile memorytechnologies used for neural network such as SRAM, DRAM, and othervolatile synapse cells, without limitation.

Neural Networks Employing Non-Volatile Memory Cell Arrays

FIG. 9 conceptually illustrates a non-limiting example of a neuralnetwork utilizing a non-volatile memory array of the present examples.This example uses the non-volatile memory array neural network for afacial recognition application, but any other appropriate applicationcould be implemented using a non-volatile memory array based neuralnetwork.

S0 is the input layer, which for this example is a 32×32 pixel RGB imagewith 5 bit precision (i.e. three 32×32 pixel arrays, one for each colorR, G and B, each pixel being 5 bit precision). The synapses CB1 goingfrom input layer S0 to layer C1 apply different sets of weights in someinstances and shared weights in other instances, and scan the inputimage with 3×3 pixel overlapping filters (kernel), shifting the filterby 1 pixel (or more than 1 pixel as dictated by the model).Specifically, values for 9 pixels in a 3×3 portion of the image (i.e.,referred to as a filter or kernel) are provided to the synapses CB1,where these 9 input values are multiplied by the appropriate weightsand, after summing the outputs of that multiplication, a single outputvalue is determined and provided by a first synapse of CB1 forgenerating a pixel of one of the layers of feature map C1. The 3×3filter is then shifted one pixel to the right within input layer S0(i.e., adding the column of three pixels on the right, and dropping thecolumn of three pixels on the left), whereby the 9 pixel values in thisnewly positioned filter are provided to the synapses CB1, where they aremultiplied by the same weights and a second single output value isdetermined by the associated synapse. This process is continued untilthe 3×3 filter scans across the entire 32×32 pixel image of input layerS0, for all three colors and for all bits (precision values). Theprocess is then repeated using different sets of weights to generate adifferent feature map of C1, until all the features maps of layer C1have been calculated.

In layer C1, in the present example, there are 16 feature maps, with30×30 pixels each. Each pixel is a new feature pixel extracted frommultiplying the inputs and kernel, and therefore each feature map is atwo dimensional array, and thus in this example layer C1 constitutes 16layers of two dimensional arrays (keeping in mind that the layers andarrays referenced herein are logical relationships, not necessarilyphysical relationships—i.e., the arrays are not necessarily oriented inphysical two dimensional arrays). Each of the 16 feature maps in layerC1 is generated by one of sixteen different sets of synapse weightsapplied to the filter scans. The C1 feature maps could all be directedto different aspects of the same image feature, such as boundaryidentification. For example, the first map (generated using a firstweight set, shared for all scans used to generate this first map) couldidentify circular edges, the second map (generated using a second weightset different from the first weight set) could identify rectangularedges, or the aspect ratio of certain features, and so on.

An activation function P1 (pooling) is applied before going from layerC1 to layer S1, which pools values from consecutive, non-overlapping 2×2regions in each feature map. The purpose of the pooling function is toaverage out the nearby location (or a max function can also be used), toreduce the dependence of the edge location for example and to reduce thedata size before going to the next stage. At layer S1, there are 1615×15 feature maps (i.e., sixteen different arrays of 15×15 pixelseach). The synapses CB2 going from layer S1 to layer C2 scan maps in S1with 4×4 filters, with a filter shift of 1 pixel. At layer C2, there are22 12×12 feature maps. An activation function P2 (pooling) is appliedbefore going from layer C2 to layer S2, which pools values fromconsecutive non-overlapping 2×2 regions in each feature map. At layerS2, there are 22 6×6 feature maps. An activation function (pooling) isapplied at the synapses CB3 going from layer S2 to layer C3, where everyneuron in layer C3 connects to every map in layer S2 via a respectivesynapse of CB3. At layer C3, there are 64 neurons. The synapses CB4going from layer C3 to the output layer S3 fully connects C3 to S3, i.e.every neuron in layer C3 is connected to every neuron in layer S3. Theoutput at S3 includes 10 neurons, where the highest output neurondetermines the class. This output could, for example, be indicative ofan identification or classification of the contents of the originalimage.

Each layer of synapses is implemented using an array, or a portion of anarray, of non-volatile memory cells.

FIG. 10 is a block diagram of a system that can be used for thatpurpose. VMM system 32 includes non-volatile memory cells and isutilized as the synapses (such as CB1, CB2, CB3, and CB4 in FIG. 6)between one layer and the next layer. Specifically, VMM system 32comprises VMM array 33 comprising non-volatile memory cells arranged inrows and columns, erase gate and word line gate decoder 34, control gatedecoder 35, bit line decoder 36 and source line decoder 37, which decodethe respective inputs for the non-volatile memory cell array 33. Inputto VMM array 33 can be from the erase gate and wordline gate decoder 34or from the control gate decoder 35. Source line decoder 37 in thisexample also decodes the output of VMM array 33. Alternatively, bit linedecoder 36 can decode the output of VMM array 33.

VMM array 33 serves two purposes. First, it stores the weights that willbe used by the VMM system 32. Second, VMM array 33 effectivelymultiplies the inputs by the weights stored in VMM array 33 and addsthem up per output line (source line or bit line) to produce the output,which will be the input to the next layer or input to the final layer.By performing the multiplication and addition function, VMM array 33negates the need for separate multiplication and addition logic circuitsand is also power efficient due to its in-situ memory computation.Weights of a filter (kernel) are mapped across one or multiple bitlines(columns) or one or multiple rows across single or multiple VMM arrays33.

The output of VMM array 33 is supplied to a differential summer (such asa summing op-amp or a summing current mirror) 38, which sums up theoutputs of VMM array 33 to create a single value for that convolution.The differential summer 38 is arranged to perform summation of bothpositive weight and negative weight inputs to output the single value.

The summed up output values of differential summer 38 are then suppliedto an activation function circuit 39, which rectifies the output. Theactivation function circuit 39 may provide sigmoid, tanh, ReLUfunctions, or any other non-linear function. The rectified output valuesof activation function circuit 39 become an element of a feature map ofthe next layer (e.g. C1 in FIG. 9), and are then applied to the nextsynapse to produce the next feature map layer or final layer. Therefore,in this example, VMM array 33 constitutes a plurality of synapses (whichreceive their inputs from the prior layer of neurons or from an inputlayer such as an image database), and summer 38 and activation functioncircuit 39 constitute a plurality of neurons.

The input to VMM system 32 in FIG. 10 (WLx, EGx, CGx, and optionally BLxand SLx) can be analog level, binary level, digital timing pulses (inwhich case a pulses-to-analog converter PAC may be needed to convertpulses to the appropriate input analog level) or digital bits (in whichcase a DAC is provided to convert digital bits to appropriate inputanalog level) and the output can be analog level (e.g., current,voltage, or charge), binary level, digital pulses, or digital bits (inwhich case an output ADC is provided to convert output analog level intodigital bits).

FIG. 11 is a block diagram depicting the usage of numerous layers of VMMsystems 32, here labeled as VMM systems 32 a, 32 b, 32 c, 32 d, and 32e. As shown in FIG. 11, the input, denoted Inputx, is converted fromdigital to analog by a digital-to-analog converter 31, and provided toinput VMM system 32 a. The converted analog inputs could be voltage orcurrent. The input D/A conversion for the first layer could be done byusing a function or a LUT (look up table) that maps the inputs Inputx toappropriate analog levels for the matrix multiplier of input VMM system32 a. The input conversion could also be done by an analog to analog(A/A) converter to convert an external analog input to a mapped analoginput to the input VMM system 32 a. The input conversion could also bedone by a digital-to-digital pules (D/P) converter to convert anexternal digital input to a mapped digital pulse or pulses to the inputVMM system 32 a.

The output generated by input VMM system 32 a is provided as an input tothe next VMM system (hidden level 1) 32 b, which in turn generates anoutput that is provided as an input to the next VMM system (hidden level2) 32 c, and so on. The various layers of VMM system 32 function asdifferent layers of synapses and neurons of a convolutional neuralnetwork (CNN). Each VMM system 32 a, 32 b, 32 c, 32 d, and 32 e can be astand-alone, physical system comprising a respective non-volatile memoryarray, or multiple VMM systems could utilize different portions of thesame physical non-volatile memory array, or multiple VMM systems couldutilize overlapping portions of the same physical non-volatile memoryarray. Each VMM system 32 a, 32 b, 32 c, 32 d, and 32 e can also be timemultiplexed for various portion of its array or neurons. The exampleshown in FIG. 11 contains five layers (32 a, 32 b, 32 c, 32 d, 32 e):one input layer (32 a), two hidden layers (32 b, 32 c), and two fullyconnected layers (32 d, 32 e). One of ordinary skill in the art willappreciate that this is merely exemplary and that a system instead couldcomprise more than two hidden layers and more than two fully connectedlayers.

VMM Arrays

FIG. 12 depicts neuron VMM array 1200, which is particularly suited formemory cells 310 as shown in FIG. 3, and is utilized as the synapses andparts of neurons between an input layer and the next layer. VMM array1200 comprises memory array 1201 of non-volatile memory cells andreference array 1202 (at the top of the array) of non-volatile referencememory cells. Alternatively, another reference array can be placed atthe bottom.

In VMM array 1200, control gate lines, such as control gate line 1203,run in a vertical direction (hence reference array 1202 in the rowdirection is orthogonal to control gate line 1203), and erase gatelines, such as erase gate line 1204, run in a horizontal direction.Here, the inputs to VMM array 1200 are provided on the control gatelines (CG0, CG1, CG2, CG3), and the output of VMM array 1200 emerges onthe source lines (SL0, SL1). In one example, only even rows are used,and in another example, only odd rows are used. The current placed oneach source line (SL0, SL1, respectively) performs a summing function ofall the currents from the memory cells connected to that particularsource line.

As described herein for neural networks, the non-volatile memory cellsof VMM array 1200 are preferably configured to operate in asub-threshold region.

The non-volatile reference memory cells and the non-volatile memorycells described herein are biased in weak inversion:

Ids=Io*e ^((Vg−Vth)/nVt) =w*Io*e ^((Vg)/nVt),

where w=e ^((−Vth)/nVt)

where Ids is the drain to source current; Vg is gate voltage on thememory cell; Vth is the (effective or equivalent) threshold voltage ofthe memory cell; Vt is thermal voltage=k*T/q with k being the Boltzmannconstant, T the temperature in Kelvin, and q the electronic charge; n isa slope factor=1+(Cdep/Cox) with Cdep=capacitance of the depletionlayer, and Cox capacitance of the gate oxide layer; Io is the memorycell current at gate voltage equal to threshold voltage, Io isproportional to (Wt/L)*u*Cox*(n−1)*Vt² where u is carrier mobility andWt and L are width and length, respectively, of the memory cell.

For an I-to-V log converter using a memory cell (such as a referencememory cell or a peripheral memory cell) or a transistor to convertinput current Ids, into an input voltage, Vg:

Vg=n*Vt*log[Ids/wp*Io]

Here, wp is w of a reference or peripheral memory cell.

For a memory array used as a vector matrix multiplier VMM array, theoutput current is:

Iout=wa*Io*e ^((Vg)/nVt), namely

Iout=(wa/wp)*Iin=W*Iin

W=wa/wp=e ^((Vthp−Vtha)/nVt)

Iin=wp*Io* e^((Vg)/nVt)

Here, wa=W of each memory cell in the memory array. Vthp is the(equivalent or effective) threshold voltage of reference memory cell (orperipheral memory cell or transistor) and Vtha is the (equivalent oreffective) threshold voltage of the array memory cell.

A wordline or control gate can be used as the input for the memory cellfor the input voltage.

Alternatively, the non-volatile memory cells of VMM arrays describedherein can be configured to operate in the linear region:

Ids=beta*(Vgs−Vth)*Vds; beta=u*Cox*Wt/L,

Wα(Vgs−Vth),

meaning weight W in the linear region is proportional to (Vgs−Vth)

A wordline or control gate or bitline or sourceline can be used as theinput for the memory cell operated in the linear region. The bitline orsourceline can be used as the output for the memory cell.

For an I-to-V linear converter, a memory cell (such as a referencememory cell or a peripheral memory cell) or a transistor operating inthe linear region or a resistor can be used to linearly convert aninput/output current into an input/output voltage.

Alternatively, the memory cells of VMM arrays described herein can beconfigured to operate in the saturation region:

Ids=½*beta*(Vgs−Vth)²; beta=u*Cox*Wt/L

Wα(Vgs−Vth)², meaning weight W is proportional to (Vgs−Vth)²

A wordline, control gate, or erase gate can be used as the input for thememory cell operated in the saturation region. The bitline or sourcelinecan be used as the output for the output neuron.

Alternatively, the memory cells of VMM arrays described herein can beused in all regions or a combination thereof (sub threshold, linear, orsaturation) for each layer or multi layers of a neural network.

FIG. 13 depicts neuron VMM array 1300, which is particularly suited formemory cells 210 as shown in FIG. 2, and is utilized as the synapsesbetween an input layer and the next layer. VMM array 1300 comprises amemory array 1303 of non-volatile memory cells, reference array 1301 offirst non-volatile reference memory cells, and reference array 1302 ofsecond non-volatile reference memory cells. Reference arrays 1301 and1302, arranged in the column direction of the array, serve to convertcurrent inputs flowing into terminals BLR0, BLR1, BLR2, and BLR3 intovoltage inputs WL0, WL1, WL2, and WL3. In effect, the first and secondnon-volatile reference memory cells are diode-connected throughmultiplexors 1314 (only partially depicted) with current inputs flowinginto them. The reference cells are tuned (e.g., programmed) to targetreference levels. The target reference levels are provided by areference mini-array matrix (not shown) or from a bandgap basedreference circuit.

Memory array 1303 serves two purposes. First, it stores the weights thatwill be used by the VMM array 1300 on respective memory cells thereof.Second, memory array 1303 effectively multiplies the inputs (i.e.current inputs provided in terminals BLR0, BLR1, BLR2, and BLR3, whichreference arrays 1301 and 1302 convert into the input voltages to supplyto wordlines WL0, WL1, WL2, and WL3) by the weights stored in the memoryarray 1303 and then adds all the results (memory cell currents) toproduce the output on the respective bit lines (BL0-BLN), which will bethe input to the next layer or input to the final layer. By performingthe multiplication and addition function, memory array 1303 negates theneed for separate multiplication and addition logic circuits and is alsopower efficient. Here, the voltage inputs are provided on the word linesWL0, WL1, WL2, and WL3, and the output emerges on the respective bitlines BL0-BLN during a read (inference) operation. The current placed oneach of the bit lines BL0-BLN performs a summing function of thecurrents from all non-volatile memory cells connected to that particularbitline.

Table No. 6 depicts operating voltages for VMM array 1300. The columnsin the table indicate the voltages placed on word lines for selectedcells, word lines for unselected cells, bit lines for selected cells,bit lines for unselected cells, source lines for selected cells, andsource lines for unselected cells, where FLT indicates floating, i.e. novoltage is imposed. The rows indicate the operations of read, erase, andprogram.

TABLE 6 Operation of VMM Array 1300 of FIG. 13: WL WL-unsel BL BL-unselSL SL-unsel Read 0.5-3.5 V −0.5 V/0 V 0.1-2 V (Ineuron) 0.6 V-2 V/FLT 0V 0 V Erase ~5-13 V 0 V 0 V 0 V 0 V 0 V Program 1-2 V −0.5 V/0 V 0.1-3uA Vinh ~2.5 V 4-10 V 0-1 V/FLT

FIG. 14 depicts neuron VMM array 1400, which is particularly suited formemory cells 210 as shown in FIG. 2, and is utilized as the synapses andparts of neurons between an input layer and the next layer. VMM array1400 comprises a memory array 1403 of non-volatile memory cells,reference array 1401 of first non-volatile reference memory cells, andreference array 1402 of second non-volatile reference memory cells.Reference arrays 1401 and 1402 run in row direction of the VMM array1400. VMM array is similar to VMM 1300 except that in VMM array 1400,the word lines run in the vertical direction. Here, the inputs areprovided on the word lines (WLA0, WLB0, WLA1, WLB2, WLA2, WLB2, WLA3,WLB3), and the output emerges on the source line (SL0, SL1) during aread operation. The current placed on each source line performs asumming function of all the currents from the memory cells connected tothat particular source line.

Table No. 7 depicts operating voltages for VMM array 1400. The columnsin the table indicate the voltages placed on word lines for selectedcells, word lines for unselected cells, bit lines for selected cells,bit lines for unselected cells, source lines for selected cells, andsource lines for unselected cells. The rows indicate the operations ofread, erase, and program.

TABLE 7 Operation of VMM Array 1400 of FIG. 14 WL WL-unsel BL BL-unselSL SL-unsel Read 0.5-3.5 V −0.5 V/0 V 0.1-2 V 0.1 V-2 V/FLT ~0.3-1 V 0 V(Ineuron) Erase ~5-13 V 0 V 0 V 0 V 0 V SL-inhibit (~4- 8 V) Program 1-2V −0.5 V/0 V 0.1-3 uA Vinh ~2.5 V 4-10 V 0-1 V/FLT

FIG. 15 depicts neuron VMM array 1500, which is particularly suited formemory cells 310 as shown in FIG. 3, and is utilized as the synapses andparts of neurons between an input layer and the next layer. VMM array1500 comprises a memory array 1503 of non-volatile memory cells,reference array 1501 of first non-volatile reference memory cells, andreference array 1502 of second non-volatile reference memory cells.Reference arrays 1501 and 1502 serve to convert current inputs flowinginto terminals BLR0, BLR1, BLR2, and BLR3 into voltage inputs CG0, CG1,CG2, and CG3. In effect, the first and second non-volatile referencememory cells are diode-connected through multiplexors 1512 (onlypartially shown) with current inputs flowing into them through BLR0,BLR1, BLR2, and BLR3. Multiplexors 1512 each include a respectivemultiplexor 1505 and a cascoding transistor 1504 to ensure a constantvoltage on the bitline (such as BLR0) of each of the first and secondnon-volatile reference memory cells during a read operation. Thereference cells are tuned to target reference levels.

Memory array 1503 serves two purposes. First, it stores the weights thatwill be used by the VMM array 1500. Second, memory array 1503effectively multiplies the inputs (current inputs provided to terminalsBLR0, BLR1, BLR2, and BLR3, for which reference arrays 1501 and 1502convert these current inputs into the input voltages to supply to thecontrol gates (CG0, CG1, CG2, and CG3) by the weights stored in thememory array and then add all the results (cell currents) to produce theoutput, which appears on BL0-BLN, and will be the input to the nextlayer or input to the final layer. By performing the multiplication andaddition function, the memory array negates the need for separatemultiplication and addition logic circuits and is also power efficient.Here, the inputs are provided on the control gate lines (CG0, CG1, CG2,and CG3), and the output emerges on the bitlines (BL0-BLN) during a readoperation. The current placed on each bitline performs a summingfunction of all the currents from the memory cells connected to thatparticular bitline.

VMM array 1500 implements uni-directional tuning for non-volatile memorycells in memory array 1503. That is, each non-volatile memory cell iserased and then partially programmed until the desired charge on thefloating gate is reached. This can be performed, for example, using theprecision programming techniques described below. If too much charge isplaced on the floating gate (such that the wrong value is stored in thecell), the cell must be erased and the sequence of partial programmingoperations must start over. As shown, two rows sharing the same erasegate (such as EG0 or EG1) need to be erased together (which is known asa page erase), and thereafter, each cell is partially programmed untilthe desired charge on the floating gate is reached.

Table No. 8 depicts operating voltages for VMM array 1500. The columnsin the table indicate the voltages placed on word lines for selectedcells, word lines for unselected cells, bit lines for selected cells,bit lines for unselected cells, control gates for selected cells,control gates for unselected cells in the same sector as the selectedcells, control gates for unselected cells in a different sector than theselected cells, erase gates for selected cells, erase gates forunselected cells, source lines for selected cells, and source lines forunselected cells. The rows indicate the operations of read, erase, andprogram.

TABLE 8 Operation of VMM Array 1500 of FIG. 15 WL- CG-unsel CG- EG- WLunsel BL BL-unsel CG same sector unsel EG unsel SL SL-unsel Read 0.5-2 V−0.5 V/ 0.1-2 V 0 V/FLT 0-2.6 V 0-2.6 V 0-2.6 V 0-2.6 V 0-2.6 V   0 V  0V 0 V (Ineuron) Erase   0 V 0 V 0 V 0 V   0 V 0-2.6 V 0-2.6 V  5-12 V0-2.6 V   0 V  0 V Program 0.7-1 V −0.5 V/ Vinh (1-2 V) 4-11 V 0-2.6 V0-2.6 V 4.5-5 V 0-2.6 V 4.5-5 V 0-1 V 0 V 0.1-1 uA

FIG. 16 depicts neuron VMM array 1600, which is particularly suited formemory cells 310 as shown in FIG. 3, and is utilized as the synapses andparts of neurons between an input layer and the next layer. VMM array1600 comprises a memory array 1603 of non-volatile memory cells,reference array 1601 or first non-volatile reference memory cells, andreference array 1602 of second non-volatile reference memory cells. EGlines EGRO, EG0, EG1 and EGR1 are run vertically while CG lines CG0,CG1, CG2 and CG3 and SL lines WL0, WL1, WL2 and WL3 are runhorizontally. VMM array 1600 is similar to VMM array 1600, except thatVMM array 1600 implements bi-directional tuning, where each individualcell can be completely erased, partially programmed, and partiallyerased as needed to reach the desired amount of charge on the floatinggate due to the use of separate vertical EG lines. As shown, referencearrays 1601 and 1602 convert input current in the terminal BLR0, BLR1,BLR2, and BLR3 into control gate voltages CG0, CG1, CG2, and CG3(through the action of diode-connected reference cells throughmultiplexors 1614) to be applied to the memory cells in the rowdirection. The current output (neuron) is in the bitlines BL0-BLN, whereeach bit line sums all currents from the non-volatile memory cellsconnected to that particular bitline.

Table No. 9 depicts operating voltages for VMM array 1600. The columnsin the table indicate the voltages placed on word lines for selectedcells, word lines for unselected cells, bit lines for selected cells,bit lines for unselected cells, control gates for selected cells,control gates for unselected cells in the same sector as the selectedcells, control gates for unselected cells in a different sector than theselected cells, erase gates for selected cells, erase gates forunselected cells, source lines for selected cells, and source lines forunselected cells. The rows indicate the operations of read, erase, andprogram.

TABLE 9 Operation of VMM Array 1600 of FIG. 16 WL- BL- CG-unsel CG- EG-WL unsel BL unsel CG same sector unsel EG unsel SL SL-unsel Read 1.0-2 V−0.5 V/ 0.6-2 V 0 V/ 0-2.6 V 0-2.6 V 0-2.6 V 0-2.6 V 0-2.6 V 0 V 0 V/FLT0 V (Ineuron) FLT Erase 0 V 0 V 0 V 0 V 0 V 4-9 V 0-2.6 V 5-12 V 0-2.6 V0 V  0 V Program 0.7-1 V −0.5 V/ 0.1-1 uA Vinh 4-11 V 0-2.6 V 0-2.6 V4.5-5 V 0-2.6 V 4.5-5 V 0-1 V 0 V (1-2 V)

The input to the VMM arrays can be an analog level, a binary level,timing pulses, or digital bits and the output can be an analog level, abinary level, timing pulses, or digital bits (in this case an output ADCis needed to convert output analog level current or voltage into digitalbits).

For each memory cell in a VMM array, each weight W can be implemented bya single memory cell or by a differential cell or by two blend memorycells (average of 2 or more cells). In the differential cell case, twomemory cells are needed to implement a weight w as a differential weight(w=w+−w−). In the two blend memory cells, two memory cells are needed toimplement a weight w as an average of two cells.

It imperative to accurately and precisely program and erase non-volatilememory cells to ensure that the correct amount of charged is placed onthe floating gates to store the correct weight in the cell.

What is needed is a VMM system that allows for precise tuning on a wordor page basis, where a word typically comprises 8-64 memory cells eachstoring multiple logical bits (i.e., multi-level cells, each storing,for example, 3-8 bits) and a page comprises 64 memory cells each storingmultiple logical bits. What is further needed are high voltage circuitsto generate the required voltages. What is further needed are improvedprogramming sequences to minimize the occurrence of undesired effectssuch as erase gate disturb and control gate disturb.

SUMMARY OF THE INVENTION

Numerous examples for performing precision tuning of a page or a word ofnon-volatile memory cells in an analog neural memory are disclosed. Highvoltage circuits used to generate high voltages applied to terminals ofthe non-volatile memory cells during the precision tuning process arealso disclosed. Programming sequences for the application of thevoltages to the terminals to minimize the occurrence of disturbancesduring precision tuning are also disclosed.

In one example, an analog neural memory system comprises an array ofnon-volatile memory cells arranged into rows and columns, eachnon-volatile memory cell comprising a word line terminal, a bit lineterminal, and an erase gate terminal; a plurality of word lines, eachword line coupled to word line terminals of a row of non-volatile memorycells; a plurality of bit lines, each bit line coupled to bit lineterminals of a column of non-volatile memory cells; and a plurality oferase gate enable transistors, each erase gate enable transistor coupledto erase gate terminals of a word of non-volatile memory cells.

In another example, an analog neural memory system comprises an array ofnon-volatile memory cells arranged into rows and columns, eachnon-volatile memory cell comprising a word line terminal, a bit lineterminal, and an erase gate terminal; a plurality of word lines, eachword line coupled to word line terminals of a row of non-volatile memorycells; a plurality of bit lines, each bit line coupled to bit lineterminals of a column of non-volatile memory cells; and a plurality oferase gate enable transistors, each erase gate enable transistor coupledto erase gate terminals of a page of non-volatile memory cells, the pagecomprising two words of non-volatile memory cells in adjacent rows.

In another example, an analog neural memory system comprises an array ofnon-volatile memory cells arranged into rows and columns, eachnon-volatile memory cell comprising a word line terminal, a bit lineterminal, and an erase gate terminal; a plurality of word lines, eachword line coupled to word line terminals of a row of non-volatile memorycells; a plurality of bit lines, each bit line coupled to bit lineterminals of a column of non-volatile memory cells; and a plurality oferase gate lines, each erase gate line coupled to erase gate terminalsof a page of non-volatile memory cells, the page comprising two words ofnon-volatile memory cells in adjacent rows.

In another example, a method of performing tuning on a word or a page ofnon-volatile memory cells, the method comprises erasing the word or pageof non-volatile memory cells; performing soft programming of the word orpage of non-volatile memory cells; performing coarse programming of theword or page of non-volatile memory cells; performing fine programmingof the word or page of non-volatile memory cells; and identifying anyfast bits in the word or page of non-volatile memory cells so that thosebits can be programmed using a different sequence than the normal(non-fast and non-slow) bits.

In another example, a method of programming a word of non-volatilememory cells in an array of non-volatile memory cells arranged into rowsand columns, each non-volatile memory cell comprising a control gateterminal, a source line terminal and an erase gate terminal, the methodcomprises ramping up a voltage on control gate terminals of the word ofnon-volatile memory cells during a first time period; ramping up avoltage on the source line terminal of the word of non-volatile memorycells during a second time period after the first time period; andramping up a voltage on the erase gate line terminal of the word ofnon-volatile memory cells during a third time period after the secondtime period.

In another example, a method of programming a word of non-volatilememory cells in an array of non-volatile memory cells arranged into rowsand columns, each non-volatile memory cell comprising a control gateterminal, a source line terminal and an erase gate terminal, the methodcomprises ramping up a voltage on control gate terminals of the word ofnon-volatile memory cells to an intermediate voltage during a first timeperiod; ramping up a voltage on the source line terminal of the word ofnon-volatile memory cells during a second time period after the firsttime period; ramping up a voltage on the erase gate line terminal of theword of non-volatile memory cells during a third time period after thesecond time period; and further ramping up the voltage on control gateterminals of the word of non-volatile memory cells during a fourth timeperiod after the third time period.

In another example, a method of programming a word of non-volatilememory cells in an array of non-volatile memory cells arranged into rowsand columns, each non-volatile memory cell comprising a control gateterminal, a source line terminal and an erase gate terminal, the methodcomprises ramping up a voltage on control gate terminals of the word ofnon-volatile memory cells to a first intermediate voltage during a firsttime period; ramping up a voltage on the source line terminal of theword of non-volatile memory cells to a second intermediate voltageduring a second time period after the first time period; and ramping upa voltage on the erase gate line terminal of the word of non-volatilememory cells and further ramping up the voltage on the source lineterminals of the word of non-volatile memory cells and further rampingup the voltage on the control gate terminals of the word of non-volatilememory cells to a third intermediate voltage during a third time periodafter the second time period; and further ramping up the voltage oncontrol gate terminals of the word of non-volatile memory cells during afourth time period after the third time period.

In another example, a method of programming a word of non-volatilememory cells in an array of non-volatile memory cells arranged into rowsand columns, each non-volatile memory cell comprising a control gateterminal, a source line terminal and an erase gate terminal, the methodcomprises ramping up a voltage on the source line terminal of the wordof non-volatile memory cells during a first time period; and ramping upa voltage on the erase gate line terminal of the word of non-volatilememory cells and ramping up a voltage on control gate terminals of theword of non-volatile memory cells during a second time period after thefirst time period.

In another example, an analog neural memory system comprises an array ofnon-volatile memory cells arranged into rows and columns, eachnon-volatile memory cell comprising a word line terminal, a bit lineterminal, and an erase gate terminal; a plurality of word lines, eachword line coupled to word line terminals of a row of non-volatile memorycells; a plurality of bit lines, each bit line coupled to bit lineterminals of a column of non-volatile memory cells; and a circuitdecoder block comprising an erase gate decoder for providing an erasegate decoding signal, a control gate decoder for providing two controlgate signals, and source line decoder that provides a source linedecoding signal.

In another example, an analog neural memory system comprises an array ofnon-volatile memory cells arranged into rows and columns, eachnon-volatile memory cell comprising a word line terminal, a bit lineterminal, and an erase gate terminal; a plurality of word lines, eachword line coupled to word line terminals of a row of non-volatile memorycells; a plurality of bit lines, each bit line coupled to bit lineterminals of a column of non-volatile memory cells; and a circuitdecoder block comprising an erase gate decoder for providing an erasegate decoding signal, a control gate decoder for providing four controlgate signals, and source line decoder that provides a source linedecoding signal.

In another example, an analog neural memory system comprises an array ofnon-volatile memory cells arranged into rows and columns, eachnon-volatile memory cell comprising a word line terminal, a bit lineterminal, and an erase gate terminal; a plurality of word lines, eachword line coupled to word line terminals of a row of non-volatile memorycells; a plurality of bit lines, each bit line coupled to bit lineterminals of a column of non-volatile memory cells; and a circuitdecoder block comprising an erase gate decoder for providing two erasegate decoding signals, a control gate decoder for providing eightcontrol gate signals, and a source line decoder that provides a sourceline decoding signal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a prior art artificial neural network.

FIG. 2 depicts a prior art split gate flash memory cell.

FIG. 3 depicts another prior art split gate flash memory cell

FIG. 4 depicts another prior art split gate flash memory cell.

FIG. 5 depicts another prior art split gate flash memory cell.

FIG. 6 depicts another prior art split gate flash memory cell.

FIG. 7 depicts a prior art stacked gate flash memory cell.

FIG. 8 depicts a prior art twin split-gate memory cell.

FIG. 9 depicts different levels of an exemplary artificial neuralnetwork utilizing one or more VMM arrays.

FIG. 10 depicts a VMM system comprising a VMM array and other circuitry.

FIG. 11 depicts an exemplary artificial neural network utilizing one ormore VMM systems.

FIG. 12 depicts an example of a VMM array.

FIG. 13 depicts another example of a VMM array.

FIG. 14 depicts another example of a VMM array.

FIG. 15 depicts another example of a VMM array.

FIG. 16 depicts another example of a VMM array.

FIG. 17 depicts an example of a VMM array with erase capability on apage basis.

FIG. 18 depicts an example of a VMM array with erase capability on aword basis.

FIG. 19 depicts another example of a VMM array with erase capability ona word basis.

FIGS. 20A-20B depict a bi-directional tuning algorithm on a page or wordbasis.

FIG. 21A depicts a fast-bit algorithm.

FIG. 21B depicts a slow-bit algorithm.

FIG. 22 depicts a VMM system.

FIG. 23 depicts a high voltage generation block.

FIG. 24 depicts a high voltage maximum circuit.

FIG. 25 depicts another high voltage generation block.

FIG. 26 depicts another high voltage generation block.

FIGS. 27A-27B depicts examples of a ramp down circuit.

FIG. 28 depicts a ramp up circuit.

FIG. 29A-29C depict examples of high voltage decode circuits.

FIGS. 30A-30D depict examples of high voltage decode circuits.

FIG. 31 depicts a program sequence.

FIG. 32 depicts another program sequence.

FIG. 33 depicts another program sequence.

FIG. 34 depicts another program sequence.

FIG. 35 depicts an erase sequence.

FIGS. 36A-36C depict examples of high voltage decode circuits.

FIG. 37 depicts a high voltage latch circuit.

DETAILED DESCRIPTION OF THE INVENTION

The artificial neural networks of the present invention utilize acombination of CMOS technology and non-volatile memory arrays.

Improved VMM Systems with Page or Word-Based Tuning

FIG. 17 depicts VMM array 1700. VMM array 1700 implements bi-directionaltuning for a page of non-volatile memory cells. Here, exemplary page1701 comprises two words, each in a different row. A word includes aplurality of memory cells, e.g. 8-64. A special word may include justone cell or a few cells. Pairs of adjacent rows share a source line,such as SL0 or SL1. All cells in page 1701 share a common erase gateline that is controlled by erase gate enable transistor 1702, whichcontrols the provision of a voltage to the erase gate terminals EGW ofall cells in exemplary page set 1701. Here, all cells in page 1701 canbe erased at the same time. Thereafter, cells in page 1701 can bebi-directionally tuned through program and erase operations and somecells in page 1701 can be uni-directionally tuned through programoperation. The program operations can include the precision programmingtechniques described below with reference to FIGS. 20 and 21. If toomuch electron charge is placed on a floating gate (which would cause anincorrect current value to be stored in the cell, i.e. a current valuelower than the intended current value), the cell must be erased and thesequence of partial programming operations must start over.

FIG. 18 depicts VMM array 1800. VMM array 1800 implements bi-directionaltuning for a word of non-volatile memory cells. Here, exemplary word1801 comprises a plurality of cells in a row. All cells in word 1801share a common erase gate line that is controlled by erase gate enabletransistor 1802, which controls the provision of a voltage to the erasegate terminals of all cells in word 1801. Here, all cells in word 1801can be erased at the same time. Thereafter, cells in word 1801 can bebi-directionally tuned through program and erase operations. The programoperations can include the precision programming techniques describedbelow. If too much electron charge is placed on a floating gate (suchthat an incorrect current value is stored in the cell, i.e. a currentvalue lower than the intended current value), the cell must be erasedand the sequence of partial programming operations must start over.

FIG. 19 depicts VMM array 1900. VMM array 1900 implements bi-directionaltuning for a word of non-volatile memory cells. Here, exemplary word1901 comprises two half words of cells. Each half word belongs to a rowthat shares an erase gate. All cells in word 1901 share a common erasegate line connected to erase gate terminal EGW. Unlike in VMM array 1800and 1700, there is no erase gate enable transistor. Here, all cells inword 1901 can be erased at the same time. Thereafter, cells in word 1901can be bi-directionally tuned through program and erase operations. Theprogram operations can include the precision programming techniquesdescribed below. If too much electron charge is placed on a floatinggate (such that an incorrect current value is stored in the cell, i.e. acurrent value lower than the intended current value), the cell must beerased and the sequence of partial programming operations must startover.

Although not shown in FIGS. 17, 18, and 19, source line pulldownbitlines, tuning bitlines (used for ultra fine programming), dummybitlines, and redundant bitlines can be used, as described in U.S.Provisional Patent Application No. 62/981,757 filed on Feb. 26, 2020,and titled, “Ultra-Precise Tuning of Analog Neural Memory Cells In ADeep Learning Artificial Neural Network,” which is incorporated byreference herein.

FIG. 20A depicts tuning page/word algorithm 2000, which can be appliedto VMM arrays 1700, 1800, or 1900 in FIGS. 17-19.

First, the word or page is erased (step 2001). Second, deep programmingis performed on the un-used cells (step 2002). Deep programming is usedto program the cells into a state (or target level) that hasinsignificant cell current during a read operation, for example <pArange. Third, coarse programming is performed on the cells within theword or page (step 2003). Coarse programming is used to program thecells into a coarse target level, for example within 50-500% of thetarget with large (coarse) increment voltage and/or program currentand/or program timing. Fourth, fine programming is performed on thecells within the word or page (step 2004). Fine programming is used toprogram the cells into a fine target level, for example within +/−5-30%of the target with small (fine) increment program voltage and/or programcurrent and/or program timing. Fifth, ultrafine programming optionallyis performed on the cells within the word or page (step 2005). Ultrafineprogramming is used to program the cells into final target level withprecise very small increment voltage and/or program current and/orprogram timing. The percentage achieved within the final target level incoarse/fine/ultrafine programming is traded off versus the magnitude ofthe increment level and/or program timing to minimize noise such as fromprogram quantization noise (increment magnitude), disturb noise, variouscoupling noise, FG-FG coupling noise etc.

FIG. 20B depicts tuning page/word algorithm 2050, which can be appliedto VMM arrays 1700, 1800, or 1900 in FIGS. 17-19. Tuning page/wordalgorithm 2050 is similar to tuning/page word algorithm 2000 in FIG.20A, except that tuning page/word algorithm 2050 further includes stepsfor handling fast or slow bits. Steps 2001-2005 occur as in FIG. 20A. Instep 2006 or alternatively in step 2003, a determination is made whetherthe word or page contains any fast-bits or slow-bits (step 2006). A fastbit is a bit that requires a shorter period of programming to reach adesired level than a normal bit, and a slow bit is a bit that requires alonger period of programming to reach a desired level than a normal bit.The fast bit can be detected by monitoring the program rate (programspeed) of the cells such by my measuring delta Ir/delta tprog (currentchange over time, for example current change over K consecutivepulses)>a pre-determined R factor, for example K=2 pulses. The slow bitcan be detected by monitoring the program rate delta Ir/delta tprog<apre-determined R factor. If no, then the algorithm stops. If yes, thenthe fast-bit or slow-bit cells are identified and flagged. Thereafter,any programming of that word or page will utilize fastbit or slowbitalgorithms, as discussed below with reference to FIGS. 21A and 21B.

Applicant previously disclosed various techniques for performing coarseprogramming, fine programming, and ultrafine programming in U.S.Provisional Patent Application No. 62/981,757, filed on Feb. 26, 2020,and titled, “Ultra-Precise Tuning of Analog Neural Memory Cells in aDeep Learning Artificial Neural Network,” which is incorporated byreference herein.

FIG. 21A depicts fast-bit algorithm 2100. This is performed if a word orpage has been determined to contain one or more fast-bit cells, alsoknown as fast-bits. First, the word or page is erased or partiallyerased (step 2101). Second, the fast-bits are programmed and verified(step 2102). This is for example done with smaller than the default (orconstant) voltage increment and/or smaller than the default programcurrent and/or smaller than the default timing. Next the normal bits,i.e. all bits not flagged as fast-bits or slow-bits, are programmed andverified (step 2103). This is for example done with default setting ofcoarse/fine/ultrafine voltage increment and/or program current and/ortiming

FIG. 21B depicts slowbit algorithm 2150. This is performed if a word orpage has been determined to contain one or more slow-bit cells. First,the word or page is erased or partially erase (step 2151). Second, theslow-bit cells are programmed and verified (step 2152). This is forexample done with larger than the default voltage increment and/orlarger than the default program current and/or larger than the defaulttiming. Next the normal bits, i.e. all bits not flagged as slow-bits orfast-bits, are programmed and verified (step 2153). This is for exampledone with default setting of coarse/fine/ultrafine voltage incrementand/or program current and/or timing

In one example, the slow-bit cells are tuned first to avoid disturb toother cells in the same page/word, then fast-bit cells are tuned, andthen normal-bit cells are tuned.

In one example, the slow-bit cells are tuned first (to avoid disturb toother cells in the same page/word), then normal-bit cells are tuned, andthen fast-bit cells are tuned.

FIG. 22 depicts a block diagram of VMM system 2200. VMM system 2200comprises VMM array 2201, row decoders 2202, high voltage decoders 2203,column decoders 2204, bit line drivers 2205, input circuit 2206, outputcircuit 2207, control logic 2208, and bias generator 2209. VMM system2200 further comprises high voltage generation block 2210, whichcomprises charge pump 2211, charge pump regulator 2212, and high voltagelevel generator 2213. VMM system 2200 further comprises (program/erase,or aka weight tuning) algorithm controller 2214, analog circuitry 2215,control logic 2216, and test control logic 2217. The systems and methodsdescribed below can be implemented in VMM system 2200.

The input circuit 2206 may include circuits such as a DAC (digital toanalog converter), DPC (digital to pulses converter), AAC (analog toanalog converter, such as a current to voltage converter), PAC (pulse toanalog level converter), or any other type of converters. The inputcircuit 2206 may implement normalization, linear or non-linear up/downscaling functions, or arithmetic functions. The input circuit 2206 mayimplement a temperature compensation function for input. The inputcircuit 2206 may implement an activation function such as ReLU orsigmoid. The output circuit 2207 may include circuits such as a ADC(analog to digital converter, to convert neuron analog output to digitalbits), AAC (analog to analog converter, such as a current to voltageconverter), APC (analog to pulse(s) converter), or any other type ofconverters. The output circuit 2207 may implement an activation functionsuch as ReLU or sigmoids. The output circuit 2207 may implementstatistic normalization, regularization, up/down scaling functions,statistical rounding, or arithmetic functions (e.g., add, subtract,divide, multiply, shift, log) for neuron outputs. The output circuit2207 may implement a temperature compensation function for neuronoutputs or array outputs (such as bitline output) so as to keep powerconsumption of the array approximately constant or to improve precisionof the array (neuron) outputs such as by keeping the IV slopeapproximately the same.

FIG. 23 depicts high voltage generation block 2300, which is an exampleof high voltage generation block 2210 from FIG. 22. High voltagegeneration block 2300 comprises charge pump 2211 and charge pumpregulator 2212, which generate a variety of high voltages and arecontrolled by an enable signal here labeled as EN_CP. Charge pump 2211and charge pump regulator 2212 provide the requisite high voltages tocontrol gate high voltage generator 2301, erase gate high voltagegenerator 2302, and source line high voltage generator 2303, which arecontrolled by enable signals labeled EN_CGGEN, EN_EGGEN, and EN_SLGEN,respectively, and which provide high voltage signals to control gatelines, erase gate lines, and source lines, respectively, as neededduring program, erase, or read operations within a VMM array.

FIG. 24 depicts a high voltage maximum circuit, used to supply the highvoltage power supply, which identifies the voltage that is highestbetween a first high voltage HV1 and a second high voltage HV2 andoutputs the highest voltage. Comparator 2401 receives HV1 div and HV2div, which are level-shifted, divided down (i.e. low voltage) versionsof HV1 and HV2, respectively. Comparator 2401 outputs a high signal online COMPO if HV1 is greater than HV2, and it outputs a low signal online COMPO if HV1 is less than HV2. The output of comparator 2401 online COMPO is provided to high voltage level shifter 2402 and highvoltage level shifter 2403. If the output of comparator 2401 is low,then high voltage level shifter 2402 outputs a low signal on line 2402Band high voltage level shifter 2403 outputs a low signal on line 2403A.If the output of comparator 2401 is high, then high voltage levelshifter 2402 outputs a low signal on line 2402A and high voltage levelshifter 2403 outputs a low signal on the line 2403B. PMOS transistors2404, 2405, 2406, and 2407 are configured as shown, i.e. with their gatecoupled to a respective one of lines 2402A, 2402B, 2403A and 2403B. Ifhigh voltage level shifter 2402 outputs a low signal on the line 2402Band high voltage level shifter 2403 outputs a low signal on the line2403A, then high voltage output 2408 will be equivalent to HV2, less anyvoltage drop across PMOS transistors 2406, 2407. If high voltage levelshifter 2402 outputs a low signal on the line 2402A and high voltagelevel shifter 2403 outputs a low signal on the line 2403B, then highvoltage output 2408 will be equivalent to HV1, less any voltage dropacross PMOS transistors 2404, 2405.

FIG. 25 depicts high voltage generation block 2500, which is anotherexample of high voltage generation block 2412. Here, high voltagegeneration block 2500 comprises charge pump and regulator 2501 enabledby signal EN_CP, high voltage increment reference generator 2503, andhigh voltage buffer operational amplifier 2502. The voltage of theoutput of charge pump regulator 2503 can be controlled based on thesignals sent to the gates of the MOS transistors in high voltageincrement reference generator 2503, by trimming the portion of theoutput voltage HVSUP output by charge pump 2501 fed to the input of highvoltage op-amp HVOPA .

FIG. 26 depicts high voltage generator block 2600, which is anotherexample of high voltage generation block 2412. High voltage generationblock 2600 receives input VIN (a low voltage signal) and generatesoutput HV Output (a high voltage signal), and comprises operationalamplifier 2603, variable resistor 2601, and variable resistor 2602,where the gain (of operational amplifier 2601 is dependent on the valuesof variable resistor 2601 and/or variable resistor 2602. The highvoltage increment value is hence controlled by the value of the variableresistor 2601 and/or resistor 2602.

FIG. 27A depicts ramp down control circuit 2700, which comprises clampPMOS transistor 2701, enabling NMOS transistor 2702, and current biasNMOS transistor 2703, configured as shown. Ramp down circuit 2700receives voltage HV to be ramped down at the source of PMOS transistor2701 and generates output signal VHV DET at the drain of PMOS transistor2701, which will have a peak value of HV and will ramp down towardground in response to signal ENRMP, fed to the gate of enabling NMOStransistor 2702, changing from low to high. Output signal VHV_DET willbe ramped down from HV value to Vcas+Vt PH value by a current controlledby the current bias NMOS transistor 2703 when ENRMP equals to high.

FIG. 27B shows another ramp down control circuit 2750, which comprisescascoding NMOS transistor 2751, shunt NMOS transistor 2753 (provide rampcurrent), enabling NMOS transistor 2572, current source 2754, andcapacitor 2755. The HV node ramp down rate is controlled by I/C (=I orreference current source 2754/capacitance of capacitor 2755).

FIG. 28 depicts ramp up circuit 2800, which comprises NMOS cascodingtransistor 2801, enabling NMOS transistor 2802, current shunt NMOStransistor 2803, current source 2805, and capacitor 2804, configured asshown. Ramp up circuit 2800 controls the ramping rate of the HV node bythe ratio of I/C (=I reference current source 2805/capacitance ofcapacitor 2804) by shunting the current through the NMOS transistor2803. The ramp rate on the HV node is such that the current injectedthrough the capacitor 2804 is equal to the current source 2805.

FIGS. 29A, 29B, and 29C depict VMM high voltage decode circuits,comprising word line decoder circuit 2901, source line decoder circuit2904, and high voltage level shifter 2908, which are appropriate for usewith memory cells of the type shown in FIG. 2.

In FIG. 29A, word line decoder circuit 2901 comprises PMOS selecttransistor 2902 (controlled by signal HVO_B) and NMOS de-selecttransistor 2903 (controlled by signal HVO_B) configured as shown. HVSUPis high voltage supply such as supplied from a charge-pump andregulator. WLSUP provides voltage supply for wordline WL when HVO_B isenabled.

In FIG. 29B, source line decoder circuit 2904 comprises NMOS monitortransistors 2905 (controlled by signal HVO), driving transistor 2906(controlled by signal HVO), and de-select transistor 2907 (controlled bysignal HVO_B), configured as shown. When signal HVO is high, the voltageappearing on line SLSUP is passed through to line SL, and appears onmonitoring line SL MON. When signal HVO_B is high, line SL is pulleddown.

In FIG. 29C, high voltage level shifter 2908 receives enable signal ENand outputs high voltage signal HVO and its complement HVO_B betweenHVSUP, e.g., 12V, and HVSUP_LOW supply, e.g., 0V (when HVSUP is forexample equal to an intermediate level ˜5V) or 2.5V (when HVSUP is forexample 12V). For example HVO can be 5V and HVO_B can be 0V, or HVO canbe 12V and HVO_B can be 2.5V

FIGS. 30A-30D depict VMM high voltage decode circuits, comprising erasegate decoder circuit 3001, control gate decoder circuit 3004, sourceline decoder circuit 3007, and high voltage level shifter 3011, whichare appropriate for use with memory cells of the type shown in FIG. 3.

In FIGS. 30A and 30B, erase gate decoder circuit 3001 and control gatedecoder circuit 3004 use the same design as word line decoder circuit2901 in FIG. 29A.

In FIG. 30C, source line decoder circuit 3007 uses the same design assource line decoder circuit 2904 in FIG. 29.

In FIG. 30D, high voltage level shifter 3011 uses the same design ashigh voltage level shifter 2908 in FIG. 29.

FIGS. 31-34 depict programming sequences of voltages applied to acontrol gate terminal, source line terminal, and erase gate terminal ofone or more non-volatile memory cells during a program operation.

FIG. 31 depicts program sequence 3100, where the control gate voltage CGramps up during a first period, then the source line voltage SL ramps upduring a second period, and then the erase gate voltage EG ramps upduring a third period. All three voltages plateau at their peak valuesduring a fourth period, and then the ramping sequence is reversed, erasegate voltage EG ramps down during a fifth period, source line voltage SLramps down during a sixth period, and control gate voltage CG ramps downduring a seventh period. Program sequence 3100 minimizes erase gatedisturb occurrences.

FIG. 32 depicts program sequence 3200, where the control gate voltage CGramps up during a first period to an intermediate value, then the sourceline voltage SL ramps up during a second period to its peak value, andthen the erase gate voltage EG ramps up to a third period to its peakvalue, and then the control gate voltage CG ramps up during a fourthperiod to its peak value. All three voltages plateau at their peakvalues during a fifth period, and then control gate voltage CG rampsdown during a sixth period to an intermediate value, erase gate voltageEG ramps down during a seventh period, source line voltage SL ramps downduring an eighth period, and control gate voltage CG then ramps down toground during a ninth period.

FIG. 33 depicts program sequence 3300, where first the control gatevoltage CG ramps up to a first intermediate value during a first periodand then during a second period the source line voltage SL ramps up to asecond intermediate value. Then, during a third period, the control gatevoltage CG ramps up to a third intermediate value while the source linevoltage SL ramps up to its peak voltage and the erase gate voltage EGramps to its peak voltage. Finally, during a fourth period, the controlgate voltage CG ramps up to its peak voltage. Then all three voltagesplateau at their peak values during a fifth period. Then during a sixthperiod the control gate voltage CG ramps down to the third intermediatevalue, then during a seventh period the source line voltage SL rampsdown to the second intermediate value, then during an eighth period thecontrol gate voltage CG ramps down to the first intermediate value, thenduring a ninth period the erase gate voltage EG ramps down to ground,then during a tenth period the source line voltage SL ramps down toground, and then during an eleventh period the control gate voltage CGramps down to ground.

FIG. 34 depicts program sequence 3400, where the source line voltage SLramps up during a first period to a peak value, and then during a secondperiod the control gate line voltage CG ramps up to its peak value whilethe erase gate voltage EG ramps up to its peak value. Then during athird period all three voltages plateau at their peak values. Then thecontrol gate line voltage CG ramps down during a fourth period while theerase gate voltage EG ramps down, and then during a fifth period thesource line voltage SL ramps down. Program sequence 3400 minimizescontrol gate disturb occurrences.

FIG. 35 depicts erase sequence 3500, where the inhibit control gate orinhibit source line (CG-inh or SL-inh, to be applies to unselected cellsduring an operation) ramps up during a first period, and then erase gatevoltage EG ramps up during a second period. Then all voltage plateau attheir peak values during a third period. Then the erase gate voltage EGramp down during a fourth period, and then the inhibit control gate orinhibit source line (CG-inh or SL-inh) ramps down during a fifth period.This sequence is for example suitable for arrays that are suitable forbi-directional tuning such as FIG. 12, 14, 16, 19.

FIGS. 36A, 36B, and 36C depict a high voltage decoder 3600 that utilizesthe decoding sub circuit blocks 3001 (EG dec), 3004 (CG dec), 3007 (SLdec) in FIG. 30. Different arrangement of sub circuit blocks are done asshown to optimize for different configurations and optimizations.

FIG. 36A shows circuit decoder block 3601 that comprises circuit decoderblock 3602. The circuit decoder block 3602 includes EG dec that providesone EG decoding signal, CG dec that provides two CG decoding signals,and SL dec that provides one SL decoding signal.

FIG. 36B shows circuit decoder block 3611 that comprises circuit decoderblock 3612. The circuit decoder block 3612 includes EG dec that providesone EG decoding signal, CG dec that provides four CG decoding signals,and SL dec that provides one SL decoding signal.

FIG. 36C shows circuit decoder block 3621 that comprises circuit decoderblock 3622. The circuit decoder block 3622 t includes EG dec thatprovides two EG decoding signals, CG dec that provides eight CG decodingsignals, and SL dec that provides one SL decoding signal.

FIG. 37 depicts high voltage latch circuit 3700. High voltage latchcircuit 3700 comprises a cross coupled high voltage transistor inverterformed by PMOS transistors 3711 and 3712 and NMOS transistors 3713 and3714 and enabling transistors 3715 and 3716. The enabling signals ENB3706 and EN 3705 are logic signals (e.g., 0V/Vdd) when VHVSUP 3701 is atan intermediate voltage (e.g. ˜1.8-5V) and are at another intermediatevoltage (e.g., both signals EN 3705=ENB 3706=Vdd=1.8V) when VHVSUP 3701is at voltage greater than an intermediate HV voltage (e.g., >5V) andwhen VHVSUP_LOW 3702 is at an intermediate level such as 1.8V. OutputHVOUT 3704 and complementary signal HVOUTB 3703 are generated.

It should be noted that, as used herein, the terms “over” and “on” bothinclusively include “directly on” (no intermediate materials, elementsor space disposed therebetween) and “indirectly on” (intermediatematerials, elements or space disposed therebetween). Likewise, the term“adjacent” includes “directly adjacent” (no intermediate materials,elements or space disposed therebetween) and “indirectly adjacent”(intermediate materials, elements or space disposed there between),“mounted to” includes “directly mounted to” (no intermediate materials,elements or space disposed there between) and “indirectly mounted to”(intermediate materials, elements or spaced disposed there between), and“electrically coupled” includes “directly electrically coupled to” (nointermediate materials or elements there between that electricallyconnect the elements together) and “indirectly electrically coupled to”(intermediate materials or elements there between that electricallyconnect the elements together). For example, forming an element “over asubstrate” can include forming the element directly on the substratewith no intermediate materials/elements therebetween, as well as formingthe element indirectly on the substrate with one or more intermediatematerials/elements there between.

What is claimed is:
 1. A method comprising: programming a word or pageof non-volatile memory cells in an analog neural memory system; andidentifying any fast bits in the word or page of non-volatile memorycells.
 2. The method of claim 1, wherein the identifying step comprisesmonitoring the programming rate of each cell in the word or page.
 3. Themethod of claim 1, wherein the identifying step comprises monitoring thecurrent change over time during programming of each cell in the word orpage.
 4. The method of claim 1, further comprising erasing the word orpage of non-volatile memory cells.
 5. The method of claim 1, furthercomprising performing soft programming of the word or page ofnon-volatile memory cells.
 6. The method of claim 1 wherein theperforming programming step comprises coarse programming of the word orpage of non-volatile memory cells.
 7. The method of claim 6, wherein theperforming programming step comprises fine programming of the word orpage of non-volatile memory cells.
 8. The method of claim 7, furthercomprising: after the step of performing fine programming, performingultra-fine programming of the word or page of non-volatile memory cells.9. The method of claim 1, further comprising: erasing the word or pageof non-volatile memory cells; programming and verifying fast-bits in theword or page of non-volatile memory cells; and programming and verifyingnon-fast-bits in the word or page of non-volatile memory cells; whereinthe programming of the fast-bits and the programming of thenon-fast-bits utilize different programming sequences.